Sameer Agarwal 80fce72bfd Add mixed precision solves for SUITE_SPARSE
Starting with SuiteSparse version 7.4.0 CHOLMOD has support for single
precision matrices. This allows us to have single precision and mixed
precision solves when using the SUITE_SPARSE backend.

This CL also fixes sparse_cholesky_test which was completely broken for
single precision testing.

Sample performance on my Mac.
/usr/bin/time -l ./bin/bundle_adjuster --input=../../Downloads/problem-3068-310854-pre.txt
<SNIP>

Cost:
Initial                          9.099334e+07
Final                            4.161838e+06
Change                           8.683150e+07

Minimizer iterations                        6
Successful steps                            4
Unsuccessful steps                          2

Time (in seconds):
Preprocessor                         2.528222

  Residual only evaluation           0.142804 (5)
  Jacobian & residual evaluation     0.424014 (4)
  Linear solver                     54.083396 (5)
Minimizer                           54.895752

Postprocessor                        0.024564
Total                               57.448539

Termination:                   NO_CONVERGENCE (Maximum number of iterations reached. Number of iterations: 5.)

       59.04 real       341.24 user         5.49 sys
          5776375808  maximum resident set size
<SNIP>
        616329634071  instructions retired
        929475980510  cycles elapsed
          5375034560  peak memory footprint

/usr/bin/time -l ./bin/bundle_adjuster --input=../../Downloads/problem-3068-310854-pre.txt  -mixed_precision_solves
<SNIP>

Cost:
Initial                          9.099334e+07
Final                            4.148930e+06
Change                           8.684441e+07

Minimizer iterations                        6
Successful steps                            4
Unsuccessful steps                          2

Time (in seconds):
Preprocessor                         2.580217

  Residual only evaluation           0.144098 (5)
  Jacobian & residual evaluation     0.396723 (4)
  Linear solver                     23.636074 (5)
Minimizer                           24.427163

Postprocessor                        0.023790
Total                               27.031170

Termination:                   NO_CONVERGENCE (Maximum number of iterations reached. Number of iterations: 5.)

       28.58 real       128.53 user         2.37 sys
          4818386944  maximum resident set size
<SNIP>
        395186936091  instructions retired
        368802808856  cycles elapsed
          4327029824  peak memory footprint

Change-Id: I1f137b0dd12da8da7f9ced338dd8f20f4bbdf99d
2024-08-24 21:12:54 -07:00
2024-07-18 00:24:49 -07:00
2022-02-27 07:50:56 -08:00
2013-06-24 14:28:40 -07:00
2024-07-21 20:31:32 -07:00
2023-10-13 14:26:12 -07:00
2024-07-18 00:24:49 -07:00
2022-08-08 16:15:46 +03:00
2024-07-18 00:24:49 -07:00
2024-07-18 00:24:49 -07:00
2023-10-12 18:12:09 -07:00
2018-07-10 06:16:27 +00:00
2023-09-21 11:23:38 -07:00
2024-07-18 00:24:49 -07:00
2022-03-14 18:56:05 +01:00
2024-07-18 00:24:49 -07:00

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Ceres Solver

Ceres Solver is an open source C++ library for modeling and solving large, complicated optimization problems. It is a feature rich, mature and performant library which has been used in production at Google since 2010. Ceres Solver can solve two kinds of problems.

  1. Non-linear Least Squares problems with bounds constraints.
  2. General unconstrained optimization problems.

Please see ceres-solver.org for more information.

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